The proposed project is the development of an Artificial Intelligence system, ATTENDING, designed to critique a physician's preoperative anesthetic plan. This is a novel approach in that no other system has attempted to critique a physician's plan of treatment. ATTENDING receives as input 1) a list of a patient's underlying problems, 2) a planned surgical procedure, and 3) a proposed anesthetic plan specifying the techniques and agents to be used for premedication, induction, intubation, and maintenance of general anesthesia, or alternatively for a regional anesthetic technique. The system critiques this plan from the standpoint of the patient's underlying problems and their inherent risks. ATTENDING's design has the following features. 1) An "Augmented Decision Network" formalism is used to represent the decisions and subdecisions that go into the formulation of an anesthetic plan. 2) "Problem Management Frames" indicate how each underlying medical problem affects anesthetic management. 3) Rough estimates of the likelihood and morbidity of risk are used to guide the system's analysis 4) Potentially complex boolean expressions of multiple actual and possible risks are built up internally and manipulated in the process of evaluating alternative approaches. 5) An English prose critique of the proposed plan is produced. The project's goals are twofold: 1) computer science research goals exploring how best to integrate medical knowledge into a computer to let it flexibly critique a physician's plan, 2) the development of a clinically useful system. Potential applications of the approach are: 1) self evaluation by a physician, 2) allowing the computer to function as a clinical consultant, 3) teaching. A "skeletal: prototype of the ATTENDING system has been implemented. The proposed research will expand and refine this system into a robust medical decsion-making system. The completed system will be roughly ten times the size and complexity of the current prototype.